what is analysis of data in research

is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. According to Shamoo and Resnik (2003) various analytic procedures “provide a way of drawing inductive inferences from data and distinguishing the signal (the phenomenon of interest) from the noise (statistical fluctuations) present in the data”..

While data analysis in qualitative research can include statistical procedures, many times analysis becomes an ongoing iterative process where data is continuously collected and analyzed almost simultaneously. Indeed, researchers generally analyze for patterns in observations through the entire data collection phase (Savenye, Robinson, 2004). The form of the analysis is determined by the specific qualitative approach taken (field study, ethnography content analysis, oral history, biography, research) and the form of the data (field notes, documents, audiotape, videotape).

An essential component of ensuring data integrity is the accurate and appropriate analysis of research findings. Improper statistical analyses distort scientific findings, mislead casual readers (Shepard, 2002), and may negatively influence the public perception of research. Integrity issues are just as relevant to analysis of non-statistical data as well.

Considerations/issues in data analysis

There are a number of issues that researchers should be cognizant of with respect to data analysis. These include:

A tacit assumption of investigators is that they have received training sufficient to demonstrate a high standard of research practice. Unintentional ‘scientific misconduct' is likely the result of poor instruction and follow-up. A number of studies suggest this may be the case more often than believed (Nowak, 1994; Silverman, Manson, 2003). For example, Sica found that adequate training of physicians in medical schools in the proper design, implementation and evaluation of clinical trials is “abysmally small” (Sica, cited in Nowak, 1994). Indeed, a single course in biostatistics is the most that is usually offered (Christopher Williams, cited in Nowak, 1994).

A common practice of investigators is to defer the selection of analytic procedure to a research team ‘statistician’. Ideally, investigators should have substantially more than a basic understanding of the rationale for selecting one method of analysis over another. This can allow investigators to better supervise staff who conduct the data analyses process and make informed decisions


While methods of analysis may differ by scientific discipline, the optimal stage for determining appropriate analytic procedures occurs early in the research process and should not be an afterthought. According to Smeeton and Goda (2003), “Statistical advice should be obtained at the stage of initial planning of an investigation so that, for example, the method of sampling and design of questionnaire are appropriate”.

The chief aim of analysis is to distinguish between an event occurring as either reflecting a true effect versus a false one. Any bias occurring in the collection of the data, or selection of method of analysis, will increase the likelihood of drawing a biased inference. Bias can occur when recruitment of study participants falls below minimum number required to demonstrate statistical power or failure to maintain a sufficient follow-up period needed to demonstrate an effect (Altman, 2001).



When failing to demonstrate statistically different levels between treatment groups, investigators may resort to breaking down the analysis to smaller and smaller subgroups in order to find a difference. Although this practice may not inherently be unethical, these analyses should be proposed before beginning the study even if the intent is exploratory in nature. If it the study is exploratory in nature, the investigator should make this explicit so that readers understand that the research is more of a hunting expedition rather than being primarily theory driven. Although a researcher may not have a theory-based hypothesis for testing relationships between previously untested variables, a theory will have to be developed to explain an unanticipated finding. Indeed, in exploratory science, there are no a priori hypotheses therefore there are no hypothetical tests. Although theories can often drive the processes used in the investigation of qualitative studies, many times patterns of behavior or occurrences derived from analyzed data can result in developing new theoretical frameworks rather than determined (Savenye, Robinson, 2004).

It is conceivable that multiple statistical tests could yield a significant finding by chance alone rather than reflecting a true effect. Integrity is compromised if the investigator only reports tests with significant findings, and neglects to mention a large number of tests failing to reach significance. While access to computer-based statistical packages can facilitate application of increasingly complex analytic procedures, inappropriate uses of these packages can result in abuses as well.



Every field of study has developed its accepted practices for data analysis. Resnik (2000) states that it is prudent for investigators to follow these accepted norms. Resnik further states that the norms are ‘…based on two factors:

(1) the nature of the variables used (i.e., quantitative, comparative, or qualitative),

(2) assumptions about the population from which the data are drawn (i.e., random distribution, independence, sample size, etc.). If one uses unconventional norms, it is crucial to clearly state this is being done, and to show how this new and possibly unaccepted method of analysis is being used, as well as how it differs from other more traditional methods. For example, Schroder, Carey, and Vanable (2003) juxtapose their identification of new and powerful data analytic solutions developed to count data in the area of HIV contraction risk with a discussion of the limitations of commonly applied methods.

If one uses unconventional norms, it is crucial to clearly state this is being done, and to show how this new and possibly unaccepted method of analysis is being used, as well as how it differs from other more traditional methods. For example, Schroder, Carey, and Vanable (2003) juxtapose their identification of new and powerful data analytic solutions developed to count data in the area of HIV contraction risk with a discussion of the limitations of commonly applied methods.



While the conventional practice is to establish a standard of acceptability for statistical significance, with certain disciplines, it may also be appropriate to discuss whether attaining statistical significance has a true practical meaning, i.e., . Jeans (1992) defines ‘clinical significance’ as “the potential for research findings to make a real and important difference to clients or clinical practice, to health status or to any other problem identified as a relevant priority for the discipline”.

Kendall and Grove (1988) define clinical significance in terms of what happens when “… troubled and disordered clients are now, after treatment, not distinguishable from a meaningful and representative non-disturbed reference group”. Thompson and Noferi (2002) suggest that readers of counseling literature should expect authors to report either practical or clinical significance indices, or both, within their research reports. Shepard (2003) questions why some authors fail to point out that the magnitude of observed changes may too small to have any clinical or practical significance, “sometimes, a supposed change may be described in some detail, but the investigator fails to disclose that the trend is not statistically significant ”.

No amount of statistical analysis, regardless of the level of the sophistication, will correct poorly defined objective outcome measurements. Whether done unintentionally or by design, this practice increases the likelihood of clouding the interpretation of findings, thus potentially misleading readers.
The basis for this issue is the urgency of reducing the likelihood of statistical error. Common challenges include the exclusion of , filling in missing data, altering or otherwise changing data, data mining, and developing graphical representations of the data (Shamoo, Resnik, 2003).


At times investigators may enhance the impression of a significant finding by determining how to present (as opposed to data in its raw form), which portion of the data is shown, why, how and to whom (Shamoo, Resnik, 2003). Nowak (1994) notes that even experts do not agree in distinguishing between analyzing and massaging data. Shamoo (1989) recommends that investigators maintain a sufficient and accurate paper trail of how data was manipulated for future review.

The integrity of data analysis can be compromised by the environment or context in which data was collected i.e., face-to face interviews vs. focused group. The occurring within a dyadic relationship (interviewer-interviewee) differs from the group dynamic occurring within a focus group because of the number of participants, and how they react to each other’s responses. Since the data collection process could be influenced by the environment/context, researchers should take this into account when conducting data analysis.

Analyses could also be influenced by the method in which data was recorded. For example, research events could be documented by:

a. recording audio and/or video and transcribing later
b. either a researcher or self-administered survey
c. either or
d. preparing ethnographic field notes from a participant/observer
e. requesting that participants themselves take notes, compile and submit them to researchers.

While each methodology employed has rationale and advantages, issues of objectivity and subjectivity may be raised when data is analyzed.

During content analysis, staff researchers or ‘raters’ may use inconsistent strategies in analyzing text material. Some ‘raters’ may analyze comments as a whole while others may prefer to dissect text material by separating words, phrases, clauses, sentences or groups of sentences. Every effort should be made to reduce or eliminate inconsistencies between “raters” so that data integrity is not compromised.

A major challenge to data integrity could occur with the unmonitored supervision of inductive techniques. Content analysis requires raters to assign topics to text material (comments). The threat to integrity may arise when raters have received inconsistent training, or may have received previous training experience(s). Previous experience may affect how raters perceive the material or even perceive the nature of the analyses to be conducted. Thus one rater could assign topics or codes to material that is significantly different from another rater. Strategies to address this would include clearly stating a list of analyses procedures in the protocol manual, consistent training, and routine monitoring of raters.

Researchers performing analysis on either quantitative or qualitative analyses should be aware of challenges to reliability and validity. For example, in the area of content analysis, Gottschalk (1995) identifies three factors that can affect the reliability of analyzed data:

The potential for compromising data integrity arises when researchers cannot consistently demonstrate stability, reproducibility, or accuracy of data analysis

According Gottschalk, (1995), the validity of a content analysis study refers to the correspondence of the categories (the classification that raters’ assigned to text content) to the conclusions, and the generalizability of results to a theory (did the categories support the study’s conclusion, and was the finding adequately robust to support or be applied to a selected theoretical rationale?).

Upon coding text material for content analysis, raters must classify each code into an appropriate category of a cross-reference matrix. Relying on computer software to determine a frequency or word count can lead to inaccuracies. “One may obtain an accurate count of that word's occurrence and frequency, but not have an accurate accounting of the meaning inherent in each particular usage” (Gottschalk, 1995). Further analyses might be appropriate to discover the dimensionality of the data set or identity new meaningful underlying variables.

Whether statistical or non-statistical methods of analyses are used, researchers should be aware of the potential for compromising data integrity. While statistical analysis is typically performed on quantitative data, there are numerous analytic procedures specifically designed for qualitative material including content, thematic, and ethnographic analysis. Regardless of whether one studies quantitative or qualitative phenomena, researchers use a variety of tools to analyze data in order to test hypotheses, discern patterns of behavior, and ultimately answer research questions. Failure to understand or acknowledge data analysis issues presented can compromise data integrity.

References:

Gottschalk, L. A. (1995). Content analysis of verbal behavior: New findings and clinical applications. Hillside, NJ: Lawrence Erlbaum Associates, Inc

Jeans, M. E. (1992). Clinical significance of research: A growing concern. Canadian Journal of Nursing Research, 24, 1-4.

Lefort, S. (1993). The statistical versus clinical significance debate. Image, 25, 57-62.
Kendall, P. C., & Grove, W. (1988). Normative comparisons in therapy outcome. Behavioral Assessment, 10, 147-158.

Nowak, R. (1994). Problems in clinical trials go far beyond misconduct. Science. 264(5165): 1538-41.
Resnik, D. (2000). Statistics, ethics, and research: an agenda for educations and reform. Accountability in Research. 8: 163-88

Schroder, K.E., Carey, M.P., Venable, P.A. (2003). Methodological challenges in research on sexual risk behavior: I. Item content, scaling, and data analytic options. Ann Behav Med, 26(2): 76-103.

Shamoo, A.E., Resnik, B.R. (2003). Responsible Conduct of Research. Oxford University Press.

Shamoo, A.E. (1989). Principles of Research Data Audit. Gordon and Breach, New York.

Shepard, R.J. (2002). Ethics in exercise science research. Sports Med, 32 (3): 169-183.

Silverman, S., Manson, M. (2003). Research on teaching in physical education doctoral dissertations: a detailed investigation of focus, method, and analysis. Journal of Teaching in Physical Education, 22(3): 280-297.

Smeeton, N., Goda, D. (2003). Conducting and presenting social work research: some basic statistical considerations. Br J Soc Work, 33: 567-573.

Thompson, B., Noferi, G. 2002. Statistical, practical, clinical: How many types of significance should be considered in counseling research? Journal of Counseling & Development, 80(4):64-71.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

Prevent plagiarism. Run a free check.

Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

Is this article helpful?

Other students also liked, writing strong research questions | criteria & examples.

• What Is a Research Design | Types, Guide & Examples
• Data Collection | Definition, Methods & Examples

More interesting articles

• Between-Subjects Design | Examples, Pros, & Cons
• Cluster Sampling | A Simple Step-by-Step Guide with Examples
• Confounding Variables | Definition, Examples & Controls
• Construct Validity | Definition, Types, & Examples
• Content Analysis | Guide, Methods & Examples
• Control Groups and Treatment Groups | Uses & Examples
• Control Variables | What Are They & Why Do They Matter?
• Correlation vs. Causation | Difference, Designs & Examples
• Correlational Research | When & How to Use
• Critical Discourse Analysis | Definition, Guide & Examples
• Cross-Sectional Study | Definition, Uses & Examples
• Descriptive Research | Definition, Types, Methods & Examples
• Ethical Considerations in Research | Types & Examples
• Explanatory and Response Variables | Definitions & Examples
• Explanatory Research | Definition, Guide, & Examples
• Exploratory Research | Definition, Guide, & Examples
• External Validity | Definition, Types, Threats & Examples
• Extraneous Variables | Examples, Types & Controls
• Guide to Experimental Design | Overview, Steps, & Examples
• How Do You Incorporate an Interview into a Dissertation? | Tips
• How to Do Thematic Analysis | Step-by-Step Guide & Examples
• How to Write a Literature Review | Guide, Examples, & Templates
• How to Write a Strong Hypothesis | Steps & Examples
• Inclusion and Exclusion Criteria | Examples & Definition
• Independent vs. Dependent Variables | Definition & Examples
• Inductive Reasoning | Types, Examples, Explanation
• Inductive vs. Deductive Research Approach | Steps & Examples
• Internal Validity in Research | Definition, Threats, & Examples
• Internal vs. External Validity | Understanding Differences & Threats
• Longitudinal Study | Definition, Approaches & Examples
• Mediator vs. Moderator Variables | Differences & Examples
• Mixed Methods Research | Definition, Guide & Examples
• Multistage Sampling | Introductory Guide & Examples
• Naturalistic Observation | Definition, Guide & Examples
• Operationalization | A Guide with Examples, Pros & Cons
• Population vs. Sample | Definitions, Differences & Examples
• Primary Research | Definition, Types, & Examples
• Qualitative vs. Quantitative Research | Differences, Examples & Methods
• Quasi-Experimental Design | Definition, Types & Examples
• Questionnaire Design | Methods, Question Types & Examples
• Random Assignment in Experiments | Introduction & Examples
• Random vs. Systematic Error | Definition & Examples
• Reliability vs. Validity in Research | Difference, Types and Examples
• Reproducibility vs Replicability | Difference & Examples
• Reproducibility vs. Replicability | Difference & Examples
• Sampling Methods | Types, Techniques & Examples
• Semi-Structured Interview | Definition, Guide & Examples
• Simple Random Sampling | Definition, Steps & Examples
• Single, Double, & Triple Blind Study | Definition & Examples
• Stratified Sampling | Definition, Guide & Examples
• Structured Interview | Definition, Guide & Examples
• Survey Research | Definition, Examples & Methods
• Systematic Review | Definition, Example, & Guide
• Systematic Sampling | A Step-by-Step Guide with Examples
• Textual Analysis | Guide, 3 Approaches & Examples
• The 4 Types of Reliability in Research | Definitions & Examples
• The 4 Types of Validity in Research | Definitions & Examples
• Transcribing an Interview | 5 Steps & Transcription Software
• Triangulation in Research | Guide, Types, Examples
• Types of Interviews in Research | Guide & Examples
• Types of Research Designs Compared | Guide & Examples
• Types of Variables in Research & Statistics | Examples
• Unstructured Interview | Definition, Guide & Examples
• What Is a Case Study? | Definition, Examples & Methods
• What Is a Case-Control Study? | Definition & Examples
• What Is a Cohort Study? | Definition & Examples
• What Is a Conceptual Framework? | Tips & Examples
• What Is a Controlled Experiment? | Definitions & Examples
• What Is a Double-Barreled Question?
• What Is a Focus Group? | Step-by-Step Guide & Examples
• What Is a Likert Scale? | Guide & Examples
• What Is a Prospective Cohort Study? | Definition & Examples
• What Is a Retrospective Cohort Study? | Definition & Examples
• What Is Action Research? | Definition & Examples
• What Is an Observational Study? | Guide & Examples
• What Is Concurrent Validity? | Definition & Examples
• What Is Content Validity? | Definition & Examples
• What Is Convenience Sampling? | Definition & Examples
• What Is Convergent Validity? | Definition & Examples
• What Is Criterion Validity? | Definition & Examples
• What Is Data Cleansing? | Definition, Guide & Examples
• What Is Deductive Reasoning? | Explanation & Examples
• What Is Discriminant Validity? | Definition & Example
• What Is Ecological Validity? | Definition & Examples
• What Is Ethnography? | Definition, Guide & Examples
• What Is Face Validity? | Guide, Definition & Examples
• What Is Non-Probability Sampling? | Types & Examples
• What Is Participant Observation? | Definition & Examples
• What Is Peer Review? | Types & Examples
• What Is Predictive Validity? | Examples & Definition
• What Is Probability Sampling? | Types & Examples
• What Is Purposive Sampling? | Definition & Examples
• What Is Qualitative Observation? | Definition & Examples
• What Is Qualitative Research? | Methods & Examples
• What Is Quantitative Observation? | Definition & Examples
• What Is Quantitative Research? | Definition, Uses & Methods

Get unlimited documents corrected

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

Data Analysis

Methodology chapter of your dissertation should include discussions about the methods of data analysis. You have to explain in a brief manner how you are going to analyze the primary data you will collect employing the methods explained in this chapter.

There are differences between qualitative data analysis and quantitative data analysis . In qualitative researches using interviews, focus groups, experiments etc. data analysis is going to involve identifying common patterns within the responses and critically analyzing them in order to achieve research aims and objectives.

Data analysis for quantitative studies, on the other hand, involves critical analysis and interpretation of figures and numbers, and attempts to find rationale behind the emergence of main findings. Comparisons of primary research findings to the findings of the literature review are critically important for both types of studies – qualitative and quantitative.

Data analysis methods in the absence of primary data collection can involve discussing common patterns, as well as, controversies within secondary data directly related to the research area.

John Dudovskiy

• Privacy Policy

Home » Data Interpretation – Process, Methods and Questions

Data Interpretation – Process, Methods and Questions

Table of Contents

Data Interpretation

Definition :

Data interpretation refers to the process of making sense of data by analyzing and drawing conclusions from it. It involves examining data in order to identify patterns, relationships, and trends that can help explain the underlying phenomena being studied. Data interpretation can be used to make informed decisions and solve problems across a wide range of fields, including business, science, and social sciences.

Data Interpretation Process

Here are the steps involved in the data interpretation process:

• Define the research question : The first step in data interpretation is to clearly define the research question. This will help you to focus your analysis and ensure that you are interpreting the data in a way that is relevant to your research objectives.
• Collect the data: The next step is to collect the data. This can be done through a variety of methods such as surveys, interviews, observation, or secondary data sources.
• Clean and organize the data : Once the data has been collected, it is important to clean and organize it. This involves checking for errors, inconsistencies, and missing data. Data cleaning can be a time-consuming process, but it is essential to ensure that the data is accurate and reliable.
• Analyze the data: The next step is to analyze the data. This can involve using statistical software or other tools to calculate summary statistics, create graphs and charts, and identify patterns in the data.
• Interpret the results: Once the data has been analyzed, it is important to interpret the results. This involves looking for patterns, trends, and relationships in the data. It also involves drawing conclusions based on the results of the analysis.
• Communicate the findings : The final step is to communicate the findings. This can involve creating reports, presentations, or visualizations that summarize the key findings of the analysis. It is important to communicate the findings in a way that is clear and concise, and that is tailored to the audience’s needs.

Types of Data Interpretation

There are various types of data interpretation techniques used for analyzing and making sense of data. Here are some of the most common types:

Descriptive Interpretation

This type of interpretation involves summarizing and describing the key features of the data. This can involve calculating measures of central tendency (such as mean, median, and mode), measures of dispersion (such as range, variance, and standard deviation), and creating visualizations such as histograms, box plots, and scatterplots.

Inferential Interpretation

This type of interpretation involves making inferences about a larger population based on a sample of the data. This can involve hypothesis testing, where you test a hypothesis about a population parameter using sample data, or confidence interval estimation, where you estimate a range of values for a population parameter based on sample data.

Predictive Interpretation

This type of interpretation involves using data to make predictions about future outcomes. This can involve building predictive models using statistical techniques such as regression analysis, time-series analysis, or machine learning algorithms.

Exploratory Interpretation

This type of interpretation involves exploring the data to identify patterns and relationships that were not previously known. This can involve data mining techniques such as clustering analysis, principal component analysis, or association rule mining.

Causal Interpretation

This type of interpretation involves identifying causal relationships between variables in the data. This can involve experimental designs, such as randomized controlled trials, or observational studies, such as regression analysis or propensity score matching.

Data Interpretation Methods

There are various methods for data interpretation that can be used to analyze and make sense of data. Here are some of the most common methods:

Statistical Analysis

This method involves using statistical techniques to analyze the data. Statistical analysis can involve descriptive statistics (such as measures of central tendency and dispersion), inferential statistics (such as hypothesis testing and confidence interval estimation), and predictive modeling (such as regression analysis and time-series analysis).

Data Visualization

This method involves using visual representations of the data to identify patterns and trends. Data visualization can involve creating charts, graphs, and other visualizations, such as heat maps or scatterplots.

Text Analysis

This method involves analyzing text data, such as survey responses or social media posts, to identify patterns and themes. Text analysis can involve techniques such as sentiment analysis, topic modeling, and natural language processing.

Machine Learning

This method involves using algorithms to identify patterns in the data and make predictions or classifications. Machine learning can involve techniques such as decision trees, neural networks, and random forests.

Qualitative Analysis

This method involves analyzing non-numeric data, such as interviews or focus group discussions, to identify themes and patterns. Qualitative analysis can involve techniques such as content analysis, grounded theory, and narrative analysis.

Geospatial Analysis

This method involves analyzing spatial data, such as maps or GPS coordinates, to identify patterns and relationships. Geospatial analysis can involve techniques such as spatial autocorrelation, hot spot analysis, and clustering.

Applications of Data Interpretation

Data interpretation has a wide range of applications across different fields, including business, healthcare, education, social sciences, and more. Here are some examples of how data interpretation is used in different applications:

• Business : Data interpretation is widely used in business to inform decision-making, identify market trends, and optimize operations. For example, businesses may analyze sales data to identify the most popular products or customer demographics, or use predictive modeling to forecast demand and adjust pricing accordingly.
• Healthcare : Data interpretation is critical in healthcare for identifying disease patterns, evaluating treatment effectiveness, and improving patient outcomes. For example, healthcare providers may use electronic health records to analyze patient data and identify risk factors for certain diseases or conditions.
• Education : Data interpretation is used in education to assess student performance, identify areas for improvement, and evaluate the effectiveness of instructional methods. For example, schools may analyze test scores to identify students who are struggling and provide targeted interventions to improve their performance.
• Social sciences : Data interpretation is used in social sciences to understand human behavior, attitudes, and perceptions. For example, researchers may analyze survey data to identify patterns in public opinion or use qualitative analysis to understand the experiences of marginalized communities.
• Sports : Data interpretation is increasingly used in sports to inform strategy and improve performance. For example, coaches may analyze performance data to identify areas for improvement or use predictive modeling to assess the likelihood of injuries or other risks.

When to use Data Interpretation

Data interpretation is used to make sense of complex data and to draw conclusions from it. It is particularly useful when working with large datasets or when trying to identify patterns or trends in the data. Data interpretation can be used in a variety of settings, including scientific research, business analysis, and public policy.

In scientific research, data interpretation is often used to draw conclusions from experiments or studies. Researchers use statistical analysis and data visualization techniques to interpret their data and to identify patterns or relationships between variables. This can help them to understand the underlying mechanisms of their research and to develop new hypotheses.

In business analysis, data interpretation is used to analyze market trends and consumer behavior. Companies can use data interpretation to identify patterns in customer buying habits, to understand market trends, and to develop marketing strategies that target specific customer segments.

In public policy, data interpretation is used to inform decision-making and to evaluate the effectiveness of policies and programs. Governments and other organizations use data interpretation to track the impact of policies and programs over time, to identify areas where improvements are needed, and to develop evidence-based policy recommendations.

In general, data interpretation is useful whenever large amounts of data need to be analyzed and understood in order to make informed decisions.

Data Interpretation Examples

Here are some real-time examples of data interpretation:

• Social media analytics : Social media platforms generate vast amounts of data every second, and businesses can use this data to analyze customer behavior, track sentiment, and identify trends. Data interpretation in social media analytics involves analyzing data in real-time to identify patterns and trends that can help businesses make informed decisions about marketing strategies and customer engagement.
• Healthcare analytics: Healthcare organizations use data interpretation to analyze patient data, track outcomes, and identify areas where improvements are needed. Real-time data interpretation can help healthcare providers make quick decisions about patient care, such as identifying patients who are at risk of developing complications or adverse events.
• Financial analysis: Real-time data interpretation is essential for financial analysis, where traders and analysts need to make quick decisions based on changing market conditions. Financial analysts use data interpretation to track market trends, identify opportunities for investment, and develop trading strategies.
• Environmental monitoring : Real-time data interpretation is important for environmental monitoring, where data is collected from various sources such as satellites, sensors, and weather stations. Data interpretation helps to identify patterns and trends that can help predict natural disasters, track changes in the environment, and inform decision-making about environmental policies.
• Traffic management: Real-time data interpretation is used for traffic management, where traffic sensors collect data on traffic flow, congestion, and accidents. Data interpretation helps to identify areas where traffic congestion is high, and helps traffic management authorities make decisions about road maintenance, traffic signal timing, and other strategies to improve traffic flow.

Data Interpretation Questions

Data Interpretation Questions samples:

• Medical : What is the correlation between a patient’s age and their risk of developing a certain disease?
• Environmental Science: What is the trend in the concentration of a certain pollutant in a particular body of water over the past 10 years?
• Finance : What is the correlation between a company’s stock price and its quarterly revenue?
• Education : What is the trend in graduation rates for a particular high school over the past 5 years?
• Marketing : What is the correlation between a company’s advertising budget and its sales revenue?
• Sports : What is the trend in the number of home runs hit by a particular baseball player over the past 3 seasons?
• Social Science: What is the correlation between a person’s level of education and their income level?

In order to answer these questions, you would need to analyze and interpret the data using statistical methods, graphs, and other visualization tools.

Purpose of Data Interpretation

The purpose of data interpretation is to make sense of complex data by analyzing and drawing insights from it. The process of data interpretation involves identifying patterns and trends, making comparisons, and drawing conclusions based on the data. The ultimate goal of data interpretation is to use the insights gained from the analysis to inform decision-making.

Data interpretation is important because it allows individuals and organizations to:

• Understand complex data : Data interpretation helps individuals and organizations to make sense of complex data sets that would otherwise be difficult to understand.
• Identify patterns and trends : Data interpretation helps to identify patterns and trends in data, which can reveal important insights about the underlying processes and relationships.
• Make informed decisions: Data interpretation provides individuals and organizations with the information they need to make informed decisions based on the insights gained from the data analysis.
• Evaluate performance : Data interpretation helps individuals and organizations to evaluate their performance over time and to identify areas where improvements can be made.
• Communicate findings: Data interpretation allows individuals and organizations to communicate their findings to others in a clear and concise manner, which is essential for informing stakeholders and making changes based on the insights gained from the analysis.

Characteristics of Data Interpretation

Here are some characteristics of data interpretation:

• Contextual : Data interpretation is always contextual, meaning that the interpretation of data is dependent on the context in which it is analyzed. The same data may have different meanings depending on the context in which it is analyzed.
• Iterative : Data interpretation is an iterative process, meaning that it often involves multiple rounds of analysis and refinement as more data becomes available or as new insights are gained from the analysis.
• Subjective : Data interpretation is often subjective, as it involves the interpretation of data by individuals who may have different perspectives and biases. It is important to acknowledge and address these biases when interpreting data.
• Analytical : Data interpretation involves the use of analytical tools and techniques to analyze and draw insights from data. These may include statistical analysis, data visualization, and other data analysis methods.
• Evidence-based : Data interpretation is evidence-based, meaning that it is based on the data and the insights gained from the analysis. It is important to ensure that the data used in the analysis is accurate, relevant, and reliable.
• Actionable : Data interpretation is actionable, meaning that it provides insights that can be used to inform decision-making and to drive action. The ultimate goal of data interpretation is to use the insights gained from the analysis to improve performance or to achieve specific goals.

Advantages of Data Interpretation

Data interpretation has several advantages, including:

• Improved decision-making: Data interpretation provides insights that can be used to inform decision-making. By analyzing data and drawing insights from it, individuals and organizations can make informed decisions based on evidence rather than intuition.
• Identification of patterns and trends: Data interpretation helps to identify patterns and trends in data, which can reveal important insights about the underlying processes and relationships. This information can be used to improve performance or to achieve specific goals.
• Evaluation of performance: Data interpretation helps individuals and organizations to evaluate their performance over time and to identify areas where improvements can be made. By analyzing data, organizations can identify strengths and weaknesses and make changes to improve their performance.
• Communication of findings: Data interpretation allows individuals and organizations to communicate their findings to others in a clear and concise manner, which is essential for informing stakeholders and making changes based on the insights gained from the analysis.
• Better resource allocation: Data interpretation can help organizations allocate resources more efficiently by identifying areas where resources are needed most. By analyzing data, organizations can identify areas where resources are being underutilized or where additional resources are needed to improve performance.
• Improved competitiveness : Data interpretation can give organizations a competitive advantage by providing insights that help to improve performance, reduce costs, or identify new opportunities for growth.

Limitations of Data Interpretation

Data interpretation has some limitations, including:

• Limited by the quality of data: The quality of data used in data interpretation can greatly impact the accuracy of the insights gained from the analysis. Poor quality data can lead to incorrect conclusions and decisions.
• Subjectivity: Data interpretation can be subjective, as it involves the interpretation of data by individuals who may have different perspectives and biases. This can lead to different interpretations of the same data.
• Limited by analytical tools: The analytical tools and techniques used in data interpretation can also limit the accuracy of the insights gained from the analysis. Different analytical tools may yield different results, and some tools may not be suitable for certain types of data.
• Time-consuming: Data interpretation can be a time-consuming process, particularly for large and complex data sets. This can make it difficult to quickly make decisions based on the insights gained from the analysis.
• Incomplete data: Data interpretation can be limited by incomplete data sets, which may not provide a complete picture of the situation being analyzed. Incomplete data can lead to incorrect conclusions and decisions.
• Limited by context: Data interpretation is always contextual, meaning that the interpretation of data is dependent on the context in which it is analyzed. The same data may have different meanings depending on the context in which it is analyzed.

Difference between Data Interpretation and Data Analysis

Data interpretation and data analysis are two different but closely related processes in data-driven decision-making.

Data analysis refers to the process of examining and examining data using statistical and computational methods to derive insights and conclusions from it. It involves cleaning, transforming, and modeling the data to uncover patterns, relationships, and trends that can help in understanding the underlying phenomena.

Data interpretation, on the other hand, refers to the process of making sense of the findings from the data analysis by contextualizing them within the larger problem domain. It involves identifying the key takeaways from the data analysis, assessing their relevance and significance to the problem at hand, and communicating the insights in a clear and actionable manner.

In short, data analysis is about uncovering insights from the data, while data interpretation is about making sense of those insights and translating them into actionable recommendations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Implications in Research – Types, Examples and...

Research Approach – Types Methods and Examples

Delimitations in Research – Types, Examples and...

Background of The Study – Examples and Writing...

Future Research – Thesis Guide

Critical Analysis – Types, Examples and Writing...

An official website of the United States government

Here's how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS. A lock ( Lock Locked padlock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

• Search Awards
• Recent Awards
• Presidential and Honorary Awards
• About Awards
• How to Manage Your Award
• Grant General Conditions
• Cooperative Agreement Conditions
• Special Conditions
• Federal Demonstration Partnership
• Policy Office Website

Note:   When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval). Some links on this page may take you to non-federal websites. Their policies may differ from this site.

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

The main goal of the project was to develop declarative approaches to enable easy and efficient causal analysis by specifying units, treatment, outcome, and covariates, over the integrated data, and declaring any underlying assumptions required in causal analysis using database view definitions.  Causality is usually studied over independent and uniform units.  However, in many situations, the units are connected by relationships; these relationships can be links in a social network, or author-paper relationships, or employee-employer relationships.  In that case the outcomes for one unit may be affected through its relationships by the outcomes of other units.   Figure 1 illustrates a collection of units and their relationships.  This project has researched techniques and methods to allow causal inference over relational data, by assuming a much simpler relational representation, as suggested  in Figure 2.   The project had three major outcomes.

First, we developed an experimental system, called HypDB, for removing bias (such as Simpson?s paradox) in OLAP queries.  A SQL query, can often be biased and lead to incorrect decisions. HypDB detects, explains, and resolves bias in decision-support queries. We gave a simple definition of a biased query, which performs a set of independence tests on the data to detect bias, proposed a novel technique that explains the bias, thus assisting an analyst in understanding what goes on, and developed an automated method for rewriting a biased query into an unbiased query.

Second, we developed Capuchin, a system that applies database repair techniques, in order to remove bias from training data. Capuchin discovers a causal model in the training data, reasons about how to modify that causal model in order to make it conform to a socially accepted notion of fairness, then repairs the training data by modifying it as little as possible in order to attain the new, corrected causal model. We developed the necessary theory based on causality theory and information theory, proposed two algorithms for database repair, and evaluated the system on several publicly available datasets, showing that, when training on the repaired data, the models will make decisions that are significantly more fair, according to several notions of fairness, yet achieve almost the same accuracy as models trained on the raw data.

Finally, developed a system of causal inference in observational relational data. Although causal inference methods for ?observed data? (not from randomized controlled experiments) have been studied in Statistics and Artificial Intelligence, they rely on the critical assumption that the units of study are sampled from a population of homogeneous units; in other words, the data can be represented in a single flat table. However, many real-world data are available in ?relational? form in multiple related tables. Basic notions used in causal analysis, such as ?units?, no longer readily apply.  For example the treatment may be applied to one table whereas the outcome may be observed in a different table.  This makes causal inference in relational data more challenging. For this purpose, we developed a declarative framework, called CaRL (Causal Relational Langauge).  The framework includes a declarative language to represent causal background knowledge and assumptions, a semantics for complex causal queries, and an algorithm for answering causal queries from the given relational data.

Last Modified: 11/18/2021 Modified by: Dan Suciu

Please report errors in award information by writing to: [email protected] .

Briefing notes for researchers - public involvement in NHS, health and social care research

• Published: 05 April 2021
• Version: V 1.0 - April 2021
• 41 min read

Briefing note one: introduction

This guidance is for researchers new to public involvement in research and just starting to consider how best to involve members of the public in their work. It will also be of interest to researchers with experience of public involvement in research who are looking to update and refresh their knowledge and skills. And it will be helpful to others interested in public involvement in research, including involvement leads. This guidance explains the different ways that members of the public are involved in research. It will help you to plan, resource and support public involvement in research. You can find more involvement resources and guidance on Learning for Involvement .

Briefing note two: what is public involvement in research?

Definitions of involvement, engagement and participation.

Researchers and others use a variety of words to describe ways of interacting with the public. The terms involvement, engagement and participation are sometimes used interchangeably but the NIHR ascribes specific meanings to these terms as follows:

Involvement

NIHR defines public involvement in research as research being carried out ‘with’ or ‘by’ members of the public rather than ‘to’, ‘about’ or ‘for’ them.  It is an active partnership between patients, carers and members of the public with researchers that influences and shapes research. When using the term ‘public’ we include patients, potential patients, carers and people who use health and social care services as well as people from specific communities and from organisations that represent people who use services. Also included are people with lived experience of one or more health conditions, whether they’re current patients or not. Here are examples of how members of the public might get involved in research:

• as  joint grant holders or co-applicants on a research project
• identifying  research priorities
• as members of a project advisory or steering group
• commenting on and developing patient information leaflets or other research materials
• undertaking interviews with research participants
• carrying out research as user and/or carer researchers

Public involvement as defined here is also sometimes referred to as Patient and Public Involvement (PPI). More information about approaches to public involvement can be found in section seven.

Where information and knowledge about research is provided and disseminated. Examples of engagement are:

• science festivals open to the public with debates and discussions on research open days at a research centre where members of the public are invited to find out about research raising
• awareness of research through media such as television programmes, newspapers and social media
• dissemination to research participants, colleagues or members of the public on the findings of a study.

Find out more about engagement through the National Coordinating Centre for Public Engagement.

Participation

• Where people take part in a research study. Examples of participation are:
• people being recruited to a clinical trial or other research study(eg to test the efficacy of a new treatment)
• completing a questionnaire or participating in a focus group as part of a research study

Find out more about how members of the public participate in NIHR research on Be Part of Research. These different activities – involvement, engagement and participation – are often linked and, although they are distinct, they can complement each other.

Briefing note three: why involve members of the public in research?

Public involvement can improve the quality and relevance of research, as well as serving the broader democratic principles of citizenship, accountability and transparency. For these reasons, public involvement is increasingly an expectation if not a requirement of research funders. Research teams do not always clearly articulate their rationale for public involvement. A clear rationale helps researchers be more specific about who they want to involve, and in what ways.

Democratic principles

People who are affected by research have a right to have a say in it. Public involvement in research is an intrinsic part of citizenship, public accountability and transparency. It can also help empower people who use health and social care services, by providing the opportunity to influence research that is relevant to them.

Providing a different perspective

Members of the public might have personal knowledge and experience relevant to your research topic or be able to provide a more general societal perspective.  They might have lived experience of one or more health conditions or of using services. Members of the public will also bring their experiences of being part of specific communities or groups.

Improving the quality of the research

Involving members of the public helps ensure that research focuses on outcomes that are important to the public.  It is also a good way of tailoring your research to the needs of specific communities.  For example, if you are seeking research participants from a particular community, involving members of that community in shaping the study design can help improve recruitment and retention of participants by ensuring that your ways of communicating and your research methods are sensitive to the needs, customs and circumstances of the community.

Making the research more relevant

Public involvement can make research more relevant for example through:

• identifying a wider set of research topics than would have been generated just by  academics and health or social care professionals suggesting ideas for new research
• ensuring research is focused on what matters to people - for example by prioritising symptoms that are of importance to patients with a particular condition
• helping to reshape and clarify the research

Interests of research funders and research organisations

Several funding bodies, as well as research ethics committees, ask grant applicants about their plans for public involvement with an expectation that if they are not involving members of the public in the research then they need to have clear reasons for this and be able to explain them. For example, NIHR has a standard application form used by all research programmes. One of the sections on the form asks applicants to describe how they have involved the public in the design and planning of their study as well as their plans for further involvement throughout the research, including plans for evaluating impact. Applicants are also asked to provide details of the budget they have allocated for public involvement in their research. Responses to these questions will be considered by the reviewers, research panels and boards (which increasingly include members of the public) and will influence funding decisions.

The Research Ethics Service will ask about the plans for public involvement in your research if you apply for ethical approval, and it will be part of their assessment process. They expect the involvement of the public in research, as it can help ensure that research planned is ethical, relevant and acceptable from a public perspective. 

Briefing note four: why members of the public get involved in research

Members of the public get involved in research for a variety of personal and social reasons. For some, these are linked to personal experiences of health or social care services and a desire to bring about change in the quality of care or to improve treatments either for themselves or for others with a similar condition. For others it might be a way to have a ‘voice’ and influence the processes that affect people’s lives. Some people have had difficult experiences and appreciate being able to do something positive with that experience. Others have had very good experiences, and see their involvement as an opportunity to ‘give something back’. Other people get involved to ensure the voice of their community/communities are represented in research. Well planned and resourced involvement in research can also be valuable to those involved by increasing their confidence and knowledge and helping them to develop new skills. healthtalk.org has a series of interesting videos where various members of the public talk about their reasons for getting involved in research, including helping others and medical science and also for personal benefits .

Briefing note five: how to involve members of the public in research

Uk standards for public involvement.

A good place to start when planning how to involve members of the public in research is the UK Standards for Public Involvement . Developed over three years by a UK-wide partnership , the Standards are a description of what good public involvement looks like. They encourage approaches and behaviours that are the hallmark of good public involvement such as flexibility, sharing and learning, and mutual respect . The Standards are for everyone doing health or social care research, and provide guidance and reassurance for practitioners working towards achieving their own best practice. The six Standards are:

• inclusive opportunities – offer public involvement opportunities that are accessible and that reach people and groups according to research needs
• working together – work together in a way that values all contributions, and that builds and sustains mutually respectful and productive relationships
• support and learning – offer and promote support and learning opportunities that build confidence and skills for public involvement in research
• governance – involve the public in research management, regulation, leadership and decision making.
• communication – use plain language for well-timed and relevant communications, as part of involvement plans and activities
• impact – Seek improvement by identifying and sharing the difference that public involvement makes to research

Below we provide more information on each Standard. The Standards were tested by over 40 individuals, groups and organisations during a year-long pilot programme, and you can find out more about these ‘test bed’ projects and how they went about implementing the UK Standards for Public Involvement in the Implementation Stories report .

Inclusive opportunities

Offer public involvement opportunities that are accessible and that reach people and groups according to research needs. Research is to be informed by a diversity of public experience and insight, so that it leads to treatments and services which reflect these needs. The questions below may help you reflect on and decide if you meet the Standard:

• are people affected by and interested in the research involved from the earliest stages?
• have barriers to involvement, such as payment for time or accessible locations for meetings been identified and addressed?
• how is information about opportunities shared, and does it appeal to different communities?
• are there fair and transparent processes for involving the public in research, and do they reflect equality and diversity duties?
• is there choice and flexibility in opportunities offered to the public?

The NIHR is committed to actively and openly supporting and promoting equality, diversity and inclusion, and this also applies to how we do public involvement . As such, this Standard is crucial for NIHR, and the research we fund.

The NIHR’s Being Inclusive in Public Involvement in Health Research guidance provides key tips for researchers and practitioners:

• check your power - try to understand power relationships within your context and your role, and how to  promote inclusion
• value the people you work with - people will bring different perspectives and opinions
• use language carefully - avoid jargon, write in plain English and be considerate
• consider inclusive locations - decide together on the best places for meetings
• listen and seek agreement
• get from A to B, perhaps via Z - be flexible
• collaborate - work with community organisations
• invest in the workplace
• commit to a relationship
• evidence, evaluate, share and reflect
• act small, think big - support people, researchers and members of the public to develop confidence, learning and skills

Find out more about EDI and accessibility:

• Tips for researchers involving unpaid carers in health and care research
• Being Inclusive in Public Involvement in Health Research guidance
• Different experiences: a framework for considering who might be involved in research guidance
• government guidelines on writing about ethnicity
• the East Midlands Academic Health Science Network PPI webpages have a number of top tips leaflets for engaging with various communities
• NIHR Plain English Summaries guidance

Working together

Work together in a way that values all contributions, and that builds and sustains mutually respectful and productive relationships. Public involvement in research is better when people work together towards a common purpose, and different perspectives are respected. The questions below may help you reflect on and decide if you meet the Standard:

• has the purpose of public involvement been jointly defined and recorded? 
• have the practical requirements and arrangements for working together been addressed?
• have all the potential different ways of working together been explored, and have these plans and activities been developed together?
• is there a shared understanding of roles, responsibilities and expectations of public involvement?
• have individuals’ influence, ideas and contributions been recognised and addressed?

Support and learning

We offer and promote support and learning that builds confidence and skills for public involvement in research. We seek to remove practical and social barriers that stop members of the public and research professionals from making the most of public involvement in research. The questions below may help you reflect on and decide if you meet the Standard:

• is there a range of support to address identified needs?
• have specific resources been designated to support learning and development opportunities for both the public, researchers, and staff?
• do the public know where to go for information and support about public involvement?
• is there a culture of learning- by- doing, building on and sharing that learning for researchers, staff and the public?

Different types of training might be:

• sharing knowledge and experiences with colleagues and peers ‘on the job’ training
• attending training sessions or courses

Support might take the form of:

• support from other team members
• a mentor with similar experience
• team meetings or one-to-one meetings with line managers
• IT support for remote working
• informal or formal mechanisms of peer support

Find out more about guidance, training and support on Learning for Involvement .

Involve the public in research management, regulation, leadership and decision making. Public involvement in research governance can help research be more transparent and gain public trust. The questions below may help you reflect on and decide if you meet the Standard:

• are public voices heard, valued and respected in decision making?
• are public involvement plans in place that are regularly monitored, reviewed and reported on?
• is there visible and accountable responsibility for public involvement throughout the organisation?
• are realistic resources (including money, staff, time) allocated for public involvement?
• is the privacy of personal information protected by collecting and using it in a suitable way?

Communications

Use plain language for well-timed and relevant communications, as part of involvement plans and activities. Communicate with a wider audience about public involvement and research, using a broad range of approaches that are accessible and appealing. The questions below may help you reflect on and decide if you meet the Standard:

• has a communications plan been developed for involvement activities?
• are the needs of different people being met through inclusive and flexible communication methods?
• are processes in place to offer, gather, act on and share feedback with the public?
• are you sharing your public involvement learning and achievements, good and bad?

Be clear with the people you want to involve. It is important that both you, as a researcher, and the people you involve have a shared and clear understanding of what they are being invited to do. Explain why you are asking people to get involved, and agree the aims of the research. It can be helpful to develop a role description and/or terms of reference so people know what is expected of them, and what they can expect from you.

Seek improvement by identifying and sharing the difference that public involvement makes to research. Understand the changes, benefits and learning gained from the insights and experiences of patients, carers and the public. The questions below may help you reflect on and decide if you meet the Standard:

• are the public involved in deciding what the assessment of impact should focus on, and the approach to take?
• is it clear what information you will collect to help assess impact, including who has been involved and how?
• are there processes in place to help reflect on public involvement?
• is there a commitment to learn from the public involvement experience and, where possible, to act on this learning?

Resource public involvement in research

To achieve good quality public involvement, as set out by the UK Public Involvement Standards, it is crucial to consider how to resource public involvement both in terms of budget and the additional time required to involve the public in your research. Some research funders, such as NIHR, will actively encourage and expect public involvement to be adequately costed in research proposals. It can be challenging to obtain funding for public involvement later if it has not been built into your research grant application. Costs will differ depending on how you are involving people in your research and who you are involving. You can find in-depth information on budgeting for involvement in the NIHR’s Payment Guidance for researchers and professionals .

Do I need ethical approval for public involvement?

Ethical approval is not needed where people are involved in planning or advising on research, for example as a co-applicant on a research grant, a member of an advisory group or in developing a questionnaire.  For more information, the following resources are recommended: Qualitative research and patient and public involvement in health and social care research: What are the key differences?

Clarify your organisational responsibilities

It is important that you liaise well in advance with the relevant departments within your organisation such as finance and human resources (HR). Explain how you plan to involve members of the public in your research. This will ensure that you are aware and informed of local arrangements for involving members of the public and any issues that finance or HR may raise about the proposed arrangements. If there are any concerns raised by these departments you will have time to address them early on. Examples of issues you might need to consider are:

• payment and expenses policies
• methods for paying people (including tax and national insurance deductions)
• confidentiality agreements –(where these apply,  all members of the research group should be asked to sign , not just the members of the public
• health and safety
• honorary contracts
• Disclosure and Barring Service (DBS) checks

We recommend that you:

• have a lead for public involvement in your team so there is a point of contact for all involved
• talk to others within your organisation who have involved people in their research
• make sure that people involved know that they can stop being involved at any time
• consider the emotional support you might need to offer people, if the research is sensitive or they find some of the information distressing, and where to access this support
• if asking your own patients or their  carers to work with you on a study consider if this is appropriate and how this might affect your relationship with them
• where appropriate, discuss in advance what will happen should people become ill for periods of the research discuss in advance with the people you have involved whether and in what ways they might want to stay involved after the project

Briefing note six: who should I involve and how do I find people to involve?

Who should i involve.

In deciding who best to involve it is important to think about the knowledge and perspectives that you are looking for from members of the public, and what support you are able to give to people who you plan to involve. You might also want to consider involving carers in your research, as they provide a unique and valuable perspective. We have produced some tips to help you involve unpaid carers in your research project . Key points to consider when deciding who might be involved in research,  are set out in the guidance note: Different Experiences: A framework for considering who might be involved in research guidance and include the following:

• be clear about the purpose of involving people in research and what experiences and knowledge they can provide
• include a diversity of relevant views and perspectives
• take a common sense approach to who you involve
• people can wear several hats, and their contributions may be broader because of this range of experiences
• the type of lived experience required will vary depending on the focus of the research

For most studies it is not appropriate for people involved in the research also to be participants in the research as that can compromise both the researcher and the person involved. The possible exceptions to this may include some participatory/action research studies where the participants of the research may also be acting as co-researchers, influencing the shape of the study as it progresses.

Involve more than one person

Involving more than one person allows you to include different people at different stages of the research, and provides them more choices in  how they are involved. It will also help you to:

• increase the breadth of experience and skills brought to the project
• provide an opportunity for those involved to support and encourage each other
• make the overall public presence more resilient to individual absences resulting from illness or clashing commitments Include a diversity of relevant views and perspectives

Consider the broad and different views and experiences you will need to include in your research. Try to ensure that you have a variety of perspectives so you get different viewpoints and allow time for those who have the skills you require for the project to network with others. You can read the Being Inclusive in Public Involvement in Health Research guidance for further information, and refer to the ‘inclusive opportunities’ Standard for public involvement .

How do I find people to involve?

Once you have considered who you would like to involve, you then need to think about how to make contact with them. Speak with colleagues and members of the public and ask for their views on how to find the people you want to involve. Allow time to make contact with organisations and individuals as finding people will nearly always take longer than you think.  Rather than inviting people in to talk to you, go out and engage with communities or groups where they already meet, whether that be in physical spaces or online forums. Others have contacted people by:

for specific communities, getting in touch with relevant community groups, community and faith leaders, or other individuals who can help you engage with specific groups of people

• asking community members or patients about people who might be interested in getting involved
• advertising in GP surgeries, outpatient departments, local newspapers and on the radio
• talking to local or national patient support groups and voluntary organisations
• searching online for relevant organisations
• using social media such as Facebook or Twitter
• talking to other health and social care professionals such as community development workers, social workers, health visitors, GP practice managers
• contacting the Patient Advice and Liaison Service (PALS) Officer based at your local NHS Trust
• putting details of the opportunity for people to get involved on the People in Research website so interested members of the public can make contact

Spend time developing networks and building relationships. This might involve visiting organisations to hear about what they do and talking to them about your research. Many researchers develop long term relationships with organisations and individuals who continue for many years past the involvement in the first research project. Be aware that some people or organisations might choose not to get involved in your work. This might be for a variety of reasons but it might be because their aims do not match yours, they do not have the time, or because the practical costs of either getting involved or finding somebody to get involved in your work is too great. Some organisations charge when they are asked to find people to get involved in research activities.   Find out more about how to find people to involve:

• watch this Youtube video from Caroline Barker, from the University Hospital Southampton NHS Foundation Trust, on Reaching new public members in a virtual world
• People in Research is a national platform to help members of the public find opportunities to get involved in research and for research organisations and researchers to advertise involvement opportunities
• Vocal creates opportunities for people to find out about, and have a voice in, health research in Greater Manchester
• Patient has a comprehensive list of national and local support groups and organisations

Briefing note seven: approaches to public involvement in research

We use the terms consultation, collaboration, co-production and user-controlled describe different broad approaches to involving people in research, associated with progressively increasing levels of power and influence for members of the public. In practice, research projects can include a combination of these four and boundaries between them are not clear cut. Moreover, each approach encompasses a range of specific methods for involving people. For example, you might work with one or two service users or carers as collaborators throughout a research project, as well as consulting with a wider group of service users on a specific aspect of the study, while some members of the public might lead on one stage of the research. How you involve people will depend on the nature of your research, as well as the different activities people decide they would like to get involved in. If it is the first time that you have involved people in research consider where public involvement will be most effective in your research. This might be in a relatively modest way at first, perhaps in just one activity or at one stage of the project. Build on the skills and experience you develop in future projects

Consultation

Consultation is when you ask members of the public for their views and use these views to inform your decision making. Consultation can be about any aspect of the research process – from identifying topics for research through to thinking about the implications of research findings. You might, for example, hold one-off meetings to ask people’s views on the importance of a study and areas that it is important to measure within the study. Research funders may consult members of the public by asking for their views on research grant applications. If this is the first time you have involved people in your research, it can be a good starting point. It can also be a way of getting the views of a larger group of people. However, think carefully about what you are asking and what you will do with the information. Be clear about these aims with the people you involve. You might find that people are frustrated at being asked for their views without a commitment from you that you will act on them. There is a danger of ‘consultation fatigue’ for individuals and organisations who have been consulted before and think that their views have been ignored. If you decide to consult people on your research we recommend that you:

• give them enough time to respond
• feedback on the actions you have taken as a result of the consultation
• ask if they would like to hear about the findings of the research

Benefits of consultation:

• it enables you to find out people’s views
• it can be useful when exploring sensitive and difficult issues
• you can get a wide range of views
• you can involve people in discussion and debate

Challenges of consultation:

• you might not get the broad views you hope for
• people might have previous bad experiences of consultation where their views were not listened to
• as the consultation is framed by your own concerns and questions, you might not get the level of insight from consultees that could emerge in
• a more two-way process
• you might require an experienced facilitator

Working more closely with members of the public, returning to ask them for further information, and developing an ongoing relationship with them, will take you towards collaboration.

Collaboration

Collaboration involves an ongoing partnership between you and the members of the public you are working with, where decisions about the research are shared. For example, members of the public might collaborate with the researchers on developing the research grant application, be members of the study advisory group and collaborate with researchers to disseminate the results of a research project. This is a broad approach that can be used in a wide range of different research activities and at many different stages of the research project. Collaboration requires commitment, openness and flexibility and it is important to plan and prepare adequately (see briefing note five on planning and preparation). Benefits of collaboration:

• helps to ensure research remains focused and relevant
• skills and perspectives of the public and the researchers can complement one another
• helps to ensure the research is ethical
• can help with recruitment and informed consent

Challenges to collaboration:

• time-consuming and involves additional cost
• researchers and the public may require training or support
• researchers need to be flexible and willing to share the control of the research

Co-production

Co-producing a research project is an approach in which researchers, practitioners and the public work together, sharing power and responsibility from the start to the end of the project, including the generation of knowledge. The assumption is that those for whom the  research is most relevant  are best placed to design and deliver it in partnership with the professionals, and have skills, knowledge and experience of equal importance. There is an overlap with the collaboration approach but what defines co-production is the values and principles that underpin it, rather than any specific tools or techniques. Indeed, there is no single formula or method for co-production and such an approach would be counter to the innovation and flexibility that is implicit in co-produced research. Rather, co-production requires that relationships are valued and nurtured, that efforts are made to redress power differentials, and that people are supported and enabled to realise their potential in carrying out their roles and responsibilities in the project. Co-producing research can include partnerships between academia and organisations representing the public as well as individual public members working with organisations, for example universities, which undertake research. The NIHR’s Guidance on co-producing a research project describes key principles and features of co-production. These are summarised below: Key principles:

• sharing of power – the research is jointly owned and people work together to achieve a joint understanding.
• including all perspectives and skills – making sure the research team includes all those who can make a contribution.
• respecting and valuing the knowledge of all those working together on the research – everyone is of equal importance.
• reciprocity – everybody benefits from working together.
• building and maintaining relationships – an emphasis on relationships is key to sharing power

Key features:

• establishing ground rules
• continuing dialogue
• joint ownership of key decisions
• a commitment to relationship building
• opportunities for personal growth and development
• flexibility
• continuous reflection
• valuing and evaluating the impact of co-producing research

The NIHR’s Guidance on co-producing a research project also presents some challenges that need to be addressed to enable more opportunities for co-producing research. These challenges touch on the topics of power (and power sharing), flexibility in research approaches and practices, the need for more guidance on co-production, and how to assess and evaluate co-produced research. Find out more about co-production:

• NIHR Guidance on co-producing a research project
• NIHR Co-Production in Action (Number One)
• NIHR Co-Production in Action (Number Two)
• NIHR Co-Production in Action (Number Three)
• BMJ: ‘Co-production of knowledge: the future’ Collection of articles (2021)
• the NIHR Research Design Service South East have produced a  podcast series on co-production
• the Co-Production Collective is a community of patients, carers, researchers, practitioners, students and anyone else who is interested in co-production

User controlled research

User controlled research is research that is actively controlled, directed and managed by service users and their service user organisations. Service users decide on the issues and questions to be looked at, as well as the way the research is designed, planned, written up and disseminated. The service users will run the research advisory or steering group and may also decide to carry out the research. Some service users make no distinction between the terms user controlled and user led, others feel that user led has a different, vaguer meaning. They see user led research as research which is meant to be led and shaped by service users but is not necessarily controlled or undertaken by them. Control in user led research in this case will rest with some other group of non-service users who also have an interest in the research, such as the commissioners of the research, the researchers or people who provide services. Examples of user-controlled research in action have highlighted several key reasons why user-controlled research is important for service users:

• a commitment to changing or improving the lives of their community of service users
• frustration with traditional research and services which exclude them
• frustration with mainstream research in failing to capture their needs or to research things they feel are important

As a researcher, you might get involved in user controlled research in several ways such as:

• training or supporting a group of service users who are undertaking their own research
• supporting user controlled research in a specific part of the research
• a user controlled organisation might commission you to carry out research under their direction

Organising and hosting meetings

Regardless of the approach you take, involving members of the public is likely to entail organising and hosting meetings. How you do this can make a huge difference to how people feel about the research and how much they are able and want to get involved in your work. Holding a meeting is only one of the ways to involve people and you may decide that this is not the best approach for your research. If you do choose meetings, you need to consider whether face-to-face, online, or a combination of the two work best for your research project, and your public contributors.

Planning for meetings: face-to-face

• explore opportunities for meeting people in their own environment, such as by attending a regular meeting of an organisation or group consider venues that are on neutral ground – venues such as hospitals or local authority departments might be associated with difficult experiences
• those who are working, have young children or who are carers might need to meet outside office hours
• make sure that there is parking and public transport nearby
• it is often better to plan for a mid-morning or early afternoon start to the meeting – this makes it easier for people if they have to travel some distance to the meeting or if they need additional time in the mornings because of their disability or health condition
• in some situations, you might need to offer overnight accommodation, in which case check if they have any special requirements for an overnight stay
• make sure meeting places, hotels and facilities are accessible to all those attending, for example if you are inviting a wheelchair user to join your committee, meet in an accessible meeting room with parking nearby and fully accessible facilities
• where possible, visit the venue in person in advance of the meeting, and ask to be shown around to check its suitability and access to all rooms, dining area, disabled toilets and the stage speaker area (if required)
• ask people if they have any special dietary requirements and let them know what refreshments you will be providing
• be mindful of practical matters such as microphones and hearing loops for people with hearing impairments or large print for people with visual impairments
• think about whether you will need interpreters for people from different ethnic groups or for sign language
• provide clear information about the meeting, timings and directions for getting to the venue well in advance and in a relevant format
• ask people if they require information posted to them or if they would like to receive it by email
• plan and prepare a budget for your meeting
• consider developing terms of reference and/or ground rules for the meeting so that those attending know why the meeting is being held and the responsibilities of those attending

Planning for meetings: online

The NIHR School for Primary Care Research (SPCR) has developed useful guidance on how to hold a PPI meeting using virtual tools , which details these key tips and tricks:

• keep it simple - use easy software and tools
• send documents in advance
• don’t chair and make minutes simultaneously
• choose appropriate software - SPCR provide specific tips for Microsoft Teams and Zoom
• promote the opportunity widely - virtual meetings  give  you the opportunity to involve people not usually involved in research
• adjust the agenda accordingly - take into consideration more frequent breaks
• have a backup plan if technology fails
• provide support before and during the meeting

There is a wide range of guidance on good practice for online meetings for involvement. Here are some of the ones we think might be most useful:

• here is an overview of the best video conferencing apps for accessibility
• National Coordinating Centre for Public Engagement Online Engagement: a guide for creating and running virtual meetings and events
• a blog by University College London on Engagement in a time of social distancing , which also includes further resources and reading
• Engaging at a distance guidance (.PDF) from Being Human, which has a useful online event checklist
• there are particular considerations to keep in mind when hosting online meetings which will be accessed by disabled people. Find out more about how to make your virtual meetings and events accessible to the disability community and how to run accessible online meetings for disabled people working and studying from home.

Conducting the meetings (face-to-face and online)

• brief the Chair and other committee members to ensure that the members of the public are welcomed and included during the meeting. You can find out more about chairing a meeting by reading the  TwoCan Associates guidance for chairs (.PDF)
• offer a pre-meeting or telephone discussion for members of the public to discuss the agenda and papers
• for face-to-face meetings, provide name badges in a large clear text font
• ask people to introduce themselves at the beginning of the meeting
• agree ground rules for how you will conduct a meeting so everybody has an equal opportunity to contribute
• members including members of the public agree to these rules of mutual respect
• make sure that everybody has an equal voice on the group
• encourage the use of clear language, explain jargon and acronyms
• ask the Chair to regularly check that people understand the language used and the content of the meeting
• plan for frequent breaks as people might need to take medication or find sitting for long periods difficult
• for face-to-face meetings, see if it is possible to have a spare room to allocate as a quiet room for those who might need to take some time out of the meeting
• consider different ways of conducting meetings, such as time in small group sessions (breakout rooms online) as well meetings in a larger
• group to allow people the opportunity to contribute in different ways
• create a mentor or buddy system to support the members of the public you are involving on an ongoing basis

After the meeting (face-to-face and online)

• share a write up of the event, including any recommendations or outcomes
• provide feedback on any recommendations or outcomes
• allow sufficient time between meetings for people to consult with their peers or their organisations if they wish to do so
• ask for feedback from members of the public involved in the meeting and if they have any suggestions for improvement for future meetings

Briefing note eight: ways that people can be involved in the different stages of the research cycle

Members of the public are getting involved in a whole range of research activities. These include helping to develop the research question, applying for funding and ethical approval, sitting on advisory groups, carrying out the research and disseminating the research findings. This section considers the different ways members of the public can get involved in the stages of the research cycle. When reading through this section, keep in mind the six UK Standards for Public Involvement : inclusive opportunities; working together; support and learning; governance; communications; impact.

Identifying and prioritising research

Involving the public in helping to identify and prioritise research questions is a powerful way of ensuring that your research priorities are aligned with those of people who have a personal stake in the subject, for example if they have the condition you are researching or use relevant services. Depending on the type of funding call you are applying for, topics might have already been decided by the research funder or commissioner. Members of the public might have been involved in the identification and prioritisation of the topics by the research funding organisation. People with lived experience -  and the organisations that represent - researchers and health and care practitioners will all have distinct, though often overlapping research priorities.  By working together you can develop a shared agenda. An active partnership will enable you to learn from each other and agree on the research questions together, and the final topic will be a shared decision between the group. Consider recording how the research questions were developed and the different influences on the questions at the beginning of the project. Researchers and research organisations use a range of different ways to work with the public to identify and prioritise research. These include:

• discussions with existing reference groups and networks
• inviting people to an event or holding a workshop or focus group
• attending meetings held by service user groups
• peer group interviews
• surveys and interviews
• asking support organisations about the feedback they get from people who use services
• using an independent facilitator (this reduces the risk of researchers influencing the agenda too much)

Sometimes it is difficult for people who are unfamiliar with research to identify research questions. It may help to first discuss problems that people experience living with their condition/s, or using treatments or services, before discussing how these might be turned into research questions.

Find out if others have worked with members of the public to identify research topics

In several areas of research including cancer, arthritis and mental health the research topics and priorities of people with these conditions have been explored and published. Find out if research has been carried out to identify the topics most relevant to patients or service users in your area of work to enable you to identify relevant research topics. The James Lind Alliance , funded by the NIHR, facilitates Priority Setting Partnerships. These partnerships bring patients, carers and health and care practitioners together to identify and prioritise the care and treatment uncertainties which they agree are the most important for research. It’s not too late to involve people if you have already identified a research topic – you can still involve the public by asking them if the topic is relevant to them. Most researchers or research organisations have a range of topics or areas that they would like to research. Members of the public can work with individual researchers, research teams or organisations to decide which topics to focus on first. Listen, respond and talk through with them how their ideas can be included in your research questions.

Commissioning research

Many funding organisations now involve members of the public in commissioning research. For example the NIHR recruits and supports patients, carers and members of the public to give feedback on health and social care research funding applications . This gives a broader perspective to the review process, by considering the issues that are important from a public and patient perspective. Involvement in commissioning can be done in a variety of ways, including:

• involving members of the public in reviewing research proposals
• having members of the public on research commissioning panels or boards
• involvement in the monitoring process of research, once funded
• user controlled organisations commissioning research

Designing and managing research

Involving members of the public in the design of research helps to ensure that the research is relevant to the needs of people and that the research questions and outcomes are clear. It strengthens the ethical basis of the research, and can help improve the recruitment and retention of volunteer participants in the study. Public involvement in the design stage of the research can:

• demonstrate to funders and commissioners that the topic is important and relevant to the end users of research and that they have been
• involved in the design of the research
• identify aspects of the research that raise ethical considerations and provide a broader set of perspectives  on solutions
• suggest ways that people can be meaningfully involved in the various stages of the research
• suggest ways to ensure that diversity and inclusion are addressed in the research, for example in ensuring that volunteers can be recruited
• from certain ethnic communities
• ensure that your recruitment process is practical and feasible
• help you to develop a budget for public involvement in your research and ensure that the time and the support needed for public involvement
• is built in to the research from the beginning
• help develop written information in user friendly and plain language, which is appropriately targeted at specific communities

Be clear with the people you are involving about the fact that the funding process can be lengthy and that the research may not get funded.

Funding to support public involvement in your research grant application

It can be challenging to obtain funding to support public involvement prior to your grant application being successful. Speak to your organisation or university to see if they have any funding for public involvement. It’s not too late to involve people if you have already developed your research grant application. You can still involve members of the public by:

• asking people to review your proposal and give written comments on any potential difficulties in your design
• taking your proposal to a local public involvement group or panel and ask for their views – your university or NHS Trust might already have a
• group or panel of people who are willing to do this
• thinking about how best to involve people at other stages of the research if your application is successful

Managing research

One of the main ways that members of the public get involved in managing research is through membership of a study steering group or management committee. Increasingly members of the public are taking a more active role in research as collaborators or in some cases as the principal investigators in studies. In these circumstances they will often be employed as a member of the research team. Involving members of the public in managing research can help to ensure that:

• a public and societal perspective is maintained throughout an individual project or a programme of work
• public involvement in the project is properly budgeted and funded
• effective support is developed for members of the public involved in the study
• advice is available on improving the recruitment of participants to the study
• there is involvement in the selection process of staff and researchers for the study – particularly helpful if they are going to be working with research participants

Find out more about public involvement in managing your research on the NIHR website . 

Undertaking the research

Members of the public can get involved in a variety of roles in carrying out the research such as:

• gathering and reviewing documentary evidence
• undertaking library-based research
• carrying out interviews
• running focus groups
• analysing and interpreting the results of research

Gathering and reviewing documentary evidence and undertaking library-based research

Members of the public can help look at different types of evidence and interpret the literature from a public perspective. 

Interviewing and running focus groups

If you are going to involve members of the public as peer interviewers (people who have direct experience of the topic being researched and who carry out interviews with other members of the public) or in running focus groups, we suggest you consider:

• who the ‘peers’ are for your research project, for example their gender, age, ethnicity or experience of using a particular service
• training and support required to carry out the role.

Analysing and interpreting the results of research

Involving members of the public in analysing and interpreting research findings can:

• help to identify themes that researchers might miss
• help check the validity of the conclusions from a public perspective
• highlight findings that are more relevant to the public

You might involve the members of the public who have been working with you on the research project to analyse and interpret the research findings. Alternatively you could hold a small event to find out the views of a wider audience.

Disseminating research

Members of the public involved in your research will want to ensure that the findings are widely disseminated so they can influence and change practice for the better. It has been found that involving people at the dissemination stage is more successful if they have also  been involved at the earlier stages of the research as they then benefit from a sense of ownership of the research and an understanding of the context and background. This means they will be more likely to disseminate the results to their networks, to help summarise the research findings in clear user-friendly language and ensure that the information is accessible to a public audience.

To encourage and support public involvement in dissemination:

• develop progress reports or newsletters to keep people informed throughout the project, reporting both negative and positive results
• feedback results to all those you consulted and collaborated with as well as participants
• work with members of the public to develop your dissemination plans – they will often have access to groups and forums that researchers are not aware of
• involve people in presenting at conferences, speaking to patients, support groups and service providers
• ask members of the public involved in your research to be co-authors in journal and newsletter articles
• acknowledge the contribution members of the public made to the research when writing journal articles and reports

Remember to include funding for public involvement in disseminating the findings in your grant application, and consider if you will need to allocate funding for developing and printing summaries and for postage. It’s not too late – if you have reached the stage of disseminating your research, there are still options open to you for involving people:

• discuss your research findings with members of the public and listen to their views
• ask for their ideas on how best to report your findings to others through networks, newsletters and different media and formats
• involve people in working with you to ensure that the information is clear and easy to understand for different audiences
• reflect on and plan how you will involve people earlier in your next project

Implementing research

Public involvement in your research can influence, support and add strength to the way your research is taken into practice. Public involvement in research often provides a route to effecting change and improvement in aspects of health and care practice that are of particular concern to people. Members of the public involved in research are often passionate to ensure that action happens as a result of the research and might be able to establish influential relationships with key agencies and policy makers.

Evaluating impact

From the beginning of your project think how you are going to monitor and evaluate public involvement and its impact throughout the project. Working with the people you involve, document and write up an evaluation of the public involvement in your research recording short and long term impacts. This will help you for future projects and provide valuable knowledge for other researchers looking to involve members of the public in their work. Help to build the evidence base and let others know about what worked well and what didn’t and the impact of public involvement in your research by:

• including the information in your research reports
• publishing information on the impact in journal articles

Find out more about reporting evaluating impact of public involvement in your study:

• PiiAF – The Public Involvement Impact Assessment Framework and Guidance (Popay, Collins et al 2013)
• What does it mean to involve consumers successfully in NHS research? A consensus study (Telford et al 2004)
• PIRICOM study: a systematic review (.PDF) (Brett et al 2010)
• Service user involvement in nursing, midwifery and health visiting research: A review of evidence and practice (Smith et al 2008)

Briefing note nine: what to do when things go wrong

In this guidance we have provided information to help you think through how best to involve people in your research. However, there will occasionally be times when things go wrong. Problems can often be sorted out by informal discussions but if you think that things are going wrong it is best to act quickly. If left unresolved, problems can get worse and affect a growing number of members of a team or organisation. Depending on what the issue is we suggest you consider some of the following:

• acknowledge that there is a problem
• listen to any concerns and openly discuss them with those involved along with any concerns that you might have
• allow space and time for all involved to reflect - public involvement in research is a learning process
• refer back to any relevant documents that you have developed such as ground rules for meetings, complaints policy, confidentiality agreements
• set a timescale for agreed change to happen
• let people know about any actions/changes/decisions
• ensure support is available if necessary
• consider using a skilled external facilitator to help with the reflection process

If you are unable to resolve issues using some of the above strategies or if either you or the member of the public feel a more independent review of the situation is required then a more formal approach should be considered. It is helpful to outline in your planning and preparation the procedure for complaints and resolving differences so that the information is clearly available from the beginning. More formal procedures might be:

• Patient Advice and Liaison Service (PALS) Officers
• university complaints procedures
• local authority complaints procedures

If you are working with people representing a non-statutory organisation, that organisation might have its own processes.

Briefing note ten: where to go for further information

Nihr information.

Learning for Involvement allows you access training and guidance on public involvement People in Research reports live opportunities for public involvement in NHS, public health and social care research, and allows you to submit your own Read the NIHR Payment Guidance for Researchers and Professionals NIHR Research Support Service (RSS) - The RSS provides free and confidential support for researchers to apply for funding and develop and deliver clinical and applied health and care research. Access to support, advice and expertise is available for all researchers in England working across  the remit of the NIHR . NIHR Clinical Research Network (CRN) - The CRN supports patients, the public and health and care organisations across England to participate in high-quality research, thereby advancing knowledge and improving care. The CRN comprises 15 Local Clinical Research Networks (LCRN) and 30 Specialties which coordinate and support the delivery of high-quality research both by geography and therapy area. National leadership and coordination is provided through the Research Delivery Network Coordinating Centre. Contact your LCRN .

How to cite this guidance

NIHR, Briefing notes for researchers, April 2021, [URL], (Accessed on: [DATE])

Acknowledgements

This resource was reviewed by the NIHR in March 2021 for accuracy and currency. The NIHR endorses this resource. The previous version was referenced as: INVOLVE (2012) Briefing notes for researchers: involving the public in NHS, public health and social care research. INVOLVE, Eastleigh.

The authors of the 2012 version of the Briefing Notes for Researchers were Helen Hayes, Sarah Buckland and Maryrose Tarpey, who used to work for the INVOLVE Coordinating Centre. Additional contributors to thank for their involvement in the development of the 2012 version are: Ann Louise Caress, Alison Ford, Lesley Roberts, Carer, Kati Turner, Derek Ward, Tracey Williamson, Sarah Howlett, Lucy Simons, Philippa Yeeles, Gill Wren, Paula Davis, Sandra Paget, Doreen Tembo, Christine Vial.

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

Are you in the American middle class? Find out with our income calculator

About half of U.S. adults (52%) lived in middle-income households in 2022, according to a Pew Research Center analysis of the most recent available government data. Roughly three-in-ten (28%) were in lower-income households and 19% were in upper-income households.

Our calculator below, updated with 2022 data, lets you find out which group you are in, and compares you with:

• Other adults in your metropolitan area
• U.S. adults overall
• U.S. adults similar to you in education, age, race or ethnicity, and marital status

Find more research about the U.S. middle class on our topic page .

Our latest analysis shows that the estimated share of adults who live in middle-income households varies widely across the 254 metropolitan areas we examined, from 42% in San Jose-Sunnyvale-Santa Clara, California, to 66% in Olympia-Lacey-Tumwater, Washington. The share of adults who live in lower-income households ranges from 16% in Bismarck, North Dakota, to 46% in Laredo, Texas. The share living in upper-income households is smallest in Muskegon-Norton Shores, Michigan (8%), and greatest in San Jose-Sunnyvale-Santa Clara, California (41%).

How the income calculator works

The calculator takes your household income and adjusts it for the size of your household. The income is revised upward for households that are below average in size and downward for those of above-average size. This way, each household’s income is made equivalent to the income of a three-person household. (Three is the whole number nearest to the  average size of a U.S. household , which was 2.5 people in 2023.)

Pew Research Center does not store or share any of the information you enter.

We use your size-adjusted household income and the cost of living in your area to determine your income tier. Middle-income households – those with an income that is two-thirds to double the U.S. median household income – had incomes ranging from about $56,600 to $169,800 in 2022. Lower-income households had incomes less than $56,600, and upper-income households had incomes greater than $169,800. (All figures are computed for three-person households, adjusted for the cost of living in a metropolitan area, and expressed in 2022 dollars.)

The following example illustrates how cost-of-living adjustment for a given area was calculated: Jackson, Tennessee, is a relatively inexpensive area, with a  price level in 2022 that was 13.0% less than the national average. The San Francisco-Oakland-Berkeley metropolitan area in California is one of the most expensive, with a price level that was 17.9% higher than the national average. Thus, to step over the national middle-class threshold of $56,600, a household in Jackson needs an income of only about $49,200, or 13.0% less than the national threshold. But a household in the San Francisco area needs an income of about $66,700, or 17.9% more than the U.S. threshold, to be considered middle class.

The income calculator encompasses 254 of 387 metropolitan areas in the United States, as defined by the Office of Management and Budget  . If you live outside of one of these 254 areas, the calculator reports the estimates for your state.

The second part of our calculator asks about your education, age, race or ethnicity, and marital status. This allows you to see how other adults who are similar to you demographically are distributed across lower-, middle- and upper-income tiers in the U.S. overall. It does not recompute your economic tier.

Note: This post and interactive calculator were originally published Dec. 9, 2015, and have been updated to reflect the Center’s new analysis.   Former Senior Researcher Rakesh Kochhar and former Research Analyst Jesse Bennett also contributed to this analysis.

The Center recently published an analysis of the distribution of the  American population across income tiers . In that analysis, the estimates of the overall shares in each income tier are slightly different, because it relies on a separate government data source and includes children as well as adults.

Pew Research Center designed this calculator as a way for users to find out, based on our analysis, where they appear in the distribution of U.S. adults by income tier, as well as how they compare with others who match their demographic profile.

The data underlying the calculator come from the 2022 American Community Survey (ACS). The ACS contains approximately 3 million records, or about 1% of the U.S. population.

In our analysis, “middle-income” Americans are adults whose annual household income is two-thirds to double the national median, after incomes have been adjusted for household size. Lower-income households have incomes less than two-thirds of the median, and upper-income households have incomes more than double the median. American adults refers to those ages 18 and older who reside in a household (as opposed to group quarters).

In 2022, the  national  middle-income range was about $56,600 to $169,800 annually for a household of three. Lower-income households had incomes less than $56,600, and upper-income households had incomes greater than $169,800. (Incomes are calculated in 2022 dollars.) The median adjusted household income used to derive this middle-income range is based on household heads, regardless of their age.

These income ranges vary with the cost of living in metropolitan areas and with household size. A household in a metropolitan area with a higher-than-average cost of living, or one with more than three people, needs more than $56,600 to be included in the middle-income tier. Households in less expensive areas or with fewer than three people need less than $56,600 to be considered middle income. Additional details on the methodology are available in our  earlier analyses .

• Income & Wages
• Middle Class

Richard Fry is a senior researcher focusing on economics and education at Pew Research Center .

Income inequality is greater among Chinese Americans than any other Asian origin group in the U.S.

Is college worth it, 7 facts about americans and taxes, methodology: 2023 focus groups of asian americans, 1 in 10: redefining the asian american dream (short film), most popular.

901 E St. NW, Suite 300 Washington, DC 20004 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan, nonadvocacy fact tank that informs the public about the issues, attitudes and trends shaping the world. It does not take policy positions. The Center conducts public opinion polling, demographic research, computational social science research and other data-driven research. Pew Research Center is a subsidiary of The Pew Charitable Trusts , its primary funder.

© 2024 Pew Research Center

Have questions about buying, selling or renting during COVID-19? Learn more

Zillow Research aims to be the most open, authoritative source for timely and accurate housing data and unbiased insight.

• More New Listings at Major Climate Risk Now Than Five Years Ago

The Numbers

August 2024 U.S. Typical Home Value (Zillow Home Value Index)

$362,143 (2.5% YoY)

August 2024 U.S. Typical Rent (Zillow Observed Rent Index)

$2,063 (3.4% YOY)

August 2024 Change in New Listings

August 2024 Typical Mortgage Payment

• August 2024: New Home Sales Fell In August, But Remain Higher Than Expected
• August 2024 Existing Home Sales Pull Back
• Renters are Looking for Perks Like Pet Areas and Happy Hours Over Gyms and Pools
• Fed Cuts In Line With Expectations, Yields Increase On The News
• Zillow Home Value and Home Sales Forecast (August 2024)
• Rate drops and more inventory mean opportunities for buyers (August 2024 Market Report)
• Mortgage Rates Fell This Week After Lackluster Jobs Data
• Market Heat Index
• Market One-Pager

Trump's claims of a migrant crime wave are not supported by national data

WASHINGTON — When Donald Trump speaks at the southern border in Texas on Thursday, you can expect to hear him talk about “migrant crime,” a category he has coined and defined as a terrifying binge of criminal activity committed by undocumented immigrants spreading across the country.

“You know, in New York, what’s happening with crime is it’s through the roof, and it’s called ‘migrant,’” the former president said at a rally in Michigan earlier this month. “They beat up police officers. You’ve seen that they go in, they stab people, hurt people, shoot people. It’s a whole new form, and they have gangs now that are making our gangs look like small potatoes.”

Trump has undoubtedly tapped into the rising anger over crimes allegedly committed by undocumented migrants that have gained national attention — most recently, the killing of college student Laken Riley in Georgia last week, after which an undocumented migrant from Venezuela was arrested and charged with her murder, and the much-reported fight between New York police officers and a group of migrant teens.

According to a recent Pew  poll , 57% of Americans said that a large number of migrants seeking to enter the country leads to more crime. Republicans (85%) overwhelmingly say the migrant surge leads to increased crime in the U.S. A far smaller share of Democrats (31%) say the same. The poll found that 63% of Democrats say it does not have much of an impact.

But despite the former president’s campaign rhetoric, expert analysis and available data from major-city police departments show that despite several horrifying high-profile incidents, there is no evidence of a migrant-driven crime wave in the United States.

That won’t change the way Trump talks about immigrants in his bid to return to the White House, as he argues that President Joe Biden’s immigration policies are making Americans less safe. Trump says voters should hold Biden personally responsible for every crime committed by an undocumented immigrant.

An NBC News review of available 2024 crime data from the cities targeted by Texas’ “Operation Lone Star,” which buses or flies migrants from the border to major cities in the interior — shows overall crime levels dropping in those cities that have received the most migrants.

Overall crime is down year over year in  Philadelphia ,  Chicago , Denver ,  New York  and Los Angeles. Crime has risen in  Washington, D.C ., but local officials do not attribute the spike to migrants.

“This is a public perception problem. It’s always based upon these kinds of flashpoint events where an immigrant commits a crime,” explains Graham Ousey, a professor at the College of William & Mary and the co-author of “Immigration and Crime: Taking Stock.” “There’s no evidence for there being any relationship between somebody’s immigrant status and their involvement in crime.”

Ousey notes the emotional toll these incidents have taken and how they can inform public perception, saying, “They can be really egregious acts of criminality that really draw lots of attention that involve somebody who happens to be an immigrant. And if you have leaders, political leaders who are really pushing that narrative, I think that would have the tendency to sort of push up the myth.”

“At least a couple of recent studies show that undocumented immigrants are also not more likely to be involved in crime,” Ousey says — in part because of caution about their immigration status. “The individual-level studies actually show that they’re less involved than native-born citizens or second-generation immigrants.”

Another misconception often cited by critics is that crime is more prevalent in “sanctuary cities.” But a Department of Justice report found that “there was no evidence that the percentage of unauthorized or authorized immigrant population at the city level impacted shifts in the homicide rates and no evidence that immigration is connected to robbery at the city level.”

Trump’s campaign claims without evidence that those statistics obscure the problem.

“Democrat cities purposefully do not document when crimes are committed by illegal immigrants, because they don’t want American citizens to know the truth about the dangerous impact Joe Biden’s open border is having on their communities,” Karoline Leavitt, Trump campaign press secretary, said in a statement. “Nevertheless, Americans know migrant crime is a serious and growing threat; and the murder, rape, or abuse of one innocent citizen at the hands of an illegal immigrant is one too many.”

Trump has been pushing the argument that immigrants bring crime since launching his first campaign in 2015, often featuring at his rallies the family members of those who were killed by undocumented immigrants who had been drinking and driving. And his arguments are not new — opponents of immigration have long tried to make the case that migrants bring crime.

National crime data, especially pertaining to undocumented immigrants, is notoriously incomplete. The national data comes in piecemeal and can only be evaluated holistically when the annual data is released.

The data is incomplete on how many crimes each year are committed by migrants, primarily because most local police don’t record immigration status when they make arrests. But the studies that have been done on this, most recently by the University of Wisconsin-Madison, show that in Texas, where police do record immigration status, migrants commit fewer crimes per capita.

In December 2020, researchers studying Texas crime statistics found that “contrary to public perception, we observe considerably lower felony arrest rates among undocumented immigrants compared to legal immigrants and native-born U.S. citizens and find no evidence that undocumented criminality has increased in recent years.”

Olympia Sonnier is a field producer for NBC News. 

Garrett Haake is NBC News' senior Capitol Hill correspondent. He also covers the Trump campaign.